Denoising vs. deblurring: HDR imaging techniques using moving cameras

Abstract

New cameras such as the Canon EOS 7D and Pointgrey Grasshopper have 14-bit sensors. We present a theoretical analysis and a practical approach that exploit these new cameras with high-resolution quantization for reliable HDR imaging from a moving camera. Specifically, we propose a unified probabilistic formulation that allows us to analytically compare two HDR imaging alternatives: (1) deblurring a single blurry but clean image and (2) denoising a sequence of sharp but noisy images. By analyzing the uncertainty in the estimation of the HDR image, we conclude that multi-image denoising offers a more reliable solution. Our theoretical analysis assumes translational motion and spatially-invariant blur. For practice, we propose an approach that combines optical flow and image denoising algorithms for HDR imaging, which enables capturing sharp HDR images using handheld cameras for complex scenes with large depth variation. Quantitative evaluation on both synthetic and real images is presented.

Handling Noise and Outliers in Single Image Deblurring using L0 Sparsity

“…[14] also adopted L 0 regularization process for text deblurring process while including an intensity prior additionally. All of the mentioned deblurring methods generally work well when the image is noise free, but their performance deteriorates in the presence of noise, as the noise level increases [15,16,17]. It has been noticed that standard denoising methods like Wiener filtering, NLM filtering have a negative effect on the kernel estimation process [18,15].…”

“…All of the mentioned deblurring methods generally work well when the image is noise free, but their performance deteriorates in the presence of noise, as the noise level increases [15,16,17]. It has been noticed that standard denoising methods like Wiener filtering, NLM filtering have a negative effect on the kernel estimation process [18,15]. Zhong et al [19] proposed an approach based on directional filtering followed by Radon transform for accurate kernel estimation in the presence of noise.…”

Performance Limits for Computational Photography

“…Several recent works have analyzed the performance of multiplexed acquisition techniques, including the seminal work by Harwit and Sloan [7], as well as more recent techniques that incorporate signal-dependent noise into the analysis [1,6,15,17]. Recently, Cossairt et al [3] have analyzed CI systems taking into consideration the application (e.g.…”

“…We analyse only single image CI techniques. Multi-image capture techniques have been analyzed by Hasinoff et al [6] (defocus deblurring), and Zhang et al [17] (motion blurring).…”

“…To enable tractable analysis, we use an affine noise model [1,6,15,17]. We model signal independent noise as a Gaussian random variable with variance .…”